Title:Deep Spatial Autoencoders for Visuomotor Learning

Abstract: Reinforcement learning provides a powerful and flexible framework for
automated acquisition of robotic motion skills. However, applying reinforcement
learning requires a sufficiently detailed representation of the state,
including the configuration of task-relevant objects. We present an approach
that automates state-space construction by learning a state representation
directly from camera images. Our method uses a deep spatial autoencoder to
acquire a set of feature points that describe the environment for the current
task, such as the positions of objects, and then learns a motion skill with
these feature points using an efficient reinforcement learning method based on
local linear models. The resulting controller reacts continuously to the
learned feature points, allowing the robot to dynamically manipulate objects in
the world with closed-loop control. We demonstrate our method with a PR2 robot
on tasks that include pushing a free-standing toy block, picking up a bag of
rice using a spatula, and hanging a loop of rope on a hook at various
positions. In each task, our method automatically learns to track task-relevant
objects and manipulate their configuration with the robot's arm.

Comments:

Published in the International Conference on Robotics and Automation (ICRA)